-#' selectVaribles
-#' It is a function which construct, for a given lambda, the sets of
+#' selectVaribles
+#' It is a function which construct, for a given lambda, the sets of
#' relevant variables and irrelevant variables.
#'
#' @param phiInit an initial estimator for phi (size: p*m*k)
#' @param rhoInit an initial estimator for rho (size: m*m*k)
-#' @param piInit an initial estimator for pi (size : k)
+#' @param piInit an initial estimator for pi (size : k)
#' @param gamInit an initial estimator for gamma
-#' @param mini minimum number of iterations in EM algorithm
-#' @param maxi maximum number of iterations in EM algorithm
-#' @param gamma power in the penalty
+#' @param mini minimum number of iterations in EM algorithm
+#' @param maxi maximum number of iterations in EM algorithm
+#' @param gamma power in the penalty
#' @param glambda grid of regularization parameters
-#' @param X matrix of regressors
-#' @param Y matrix of responses
-#' @param thres threshold to consider a coefficient to be equal to 0
-#' @param tau threshold to say that EM algorithm has converged
+#' @param X matrix of regressors
+#' @param Y matrix of responses
+#' @param thres threshold to consider a coefficient to be equal to 0
+#' @param tau threshold to say that EM algorithm has converged
#'
#' @return
#' @export
#'
#' @examples
selectVariables <- function(phiInit,rhoInit,piInit,gamInit,
- mini,maxi,gamma,glambda,X,Y,thres,tau){
-
- dimphi <- dim(phiInit)
- p <- dimPhi[1]
- m <- dimPhi[2]
- k <- dimPhi[3]
- L <- length(glambda);
- A1 <- array(0, dim <- c(p,m+1,L))
- A2 <- array(0, dim <- c(p,m+1,L))
- Rho <- array(0, dim <- c(m,m,k,L))
- Pi <- array(0, dim <- c(k,L));
-
- # For every lambda in gridLambda, comutation of the coefficients
- for (lambdaIndex in c(1:L)) {
- Res <- EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,
- gamma,glambda[lambdaIndex],X,Y,tau);
- phi <- Res$phi
- rho <- Res$rho
- pi <- Res$pi
-
- # If a coefficient is larger than the threshold, we keep it
- selectedVariables <- array(0, dim = c(p,m))
- discardedVariables <- array(0, dim = c(p,m))
- atLeastOneSelectedVariable <- false
- for (j in c(1:p)){
- cpt <- 1
- cpt2 <-1
- for (mm in c(1:m)){
- if (max(abs(phi[j,mm,])) > thres){
- selectedVariables[j,cpt] <- mm
- cpt <- cpt+1
- atLeastOneSelectedVariable <- true
- } else{
- discardedVariables[j,cpt2] <- mm
- cpt2 <- cpt2+1
- }
- }
- }
-
- # If no coefficients have been selected, we provide the zero matrix
- # We delete zero coefficients: vec = indices of zero values
- if atLeastOneSelectedVariable{
- vec <- c()
- for (j in c(1:p)){
- if (selectedVariables(j,1) =! 0){
- vec <- c(vec,j)
- }
- }
- # Else, we provide the indices of relevant coefficients
- A1[,1,lambdaIndex] <- c(vec,rep(0,p-length(vec)))
- A1[1:length(vec),2:(m+1),lambdaIndex] <- selectedVariables[vec,]
- A2[,1,lambdaIndex] <- 1:p
- A2[,2:(m+1),lambdaIndex] <- discardedVariables
- Rho[,,,lambdaIndex] <- rho
- Pi[,lambdaIndex] <- pi
- }
-
- }
- return(res = list(A1 = A1, A2 = A2 , Rho = Rho, Pi = Pi))
-}
\ No newline at end of file
+ mini,maxi,gamma,glambda,X,Y,thres,tau)
+{
+ dimphi <- dim(phiInit)
+ p <- dimPhi[1]
+ m <- dimPhi[2]
+ k <- dimPhi[3]
+ L <- length(glambda);
+ A1 <- array(0, dim <- c(p,m+1,L))
+ A2 <- array(0, dim <- c(p,m+1,L))
+ Rho <- array(0, dim <- c(m,m,k,L))
+ Pi <- array(0, dim <- c(k,L));
+
+ # For every lambda in gridLambda, comutation of the coefficients
+ for (lambdaIndex in c(1:L))
+ {
+ Res <- EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,
+ gamma,glambda[lambdaIndex],X,Y,tau);
+ phi <- Res$phi
+ rho <- Res$rho
+ pi <- Res$pi
+
+ # If a coefficient is larger than the threshold, we keep it
+ selectedVariables <- array(0, dim = c(p,m))
+ discardedVariables <- array(0, dim = c(p,m))
+ atLeastOneSelectedVariable <- false
+ for (j in c(1:p))
+ {
+ cpt <- 1
+ cpt2 <-1
+ for (mm in c(1:m))
+ {
+ if (max(abs(phi[j,mm,])) > thres)
+ {
+ selectedVariables[j,cpt] <- mm
+ cpt <- cpt+1
+ atLeastOneSelectedVariable <- true
+ } else
+ {
+ discardedVariables[j,cpt2] <- mm
+ cpt2 <- cpt2+1
+ }
+ }
+ }
+
+ # If no coefficients have been selected, we provide the zero matrix
+ # We delete zero coefficients: vec = indices of zero values
+ if (atLeastOneSelectedVariable)
+ {
+ vec <- c()
+ for (j in c(1:p))
+ {
+ if (selectedVariables(j,1) != 0)
+ vec <- c(vec,j)
+ # Else ( NOTE: [auder] else ?! TODO: explain? )
+ # we provide the indices of relevant coefficients
+ A1[,1,lambdaIndex] <- c(vec,rep(0,p-length(vec)))
+ A1[1:length(vec),2:(m+1),lambdaIndex] <- selectedVariables[vec,]
+ A2[,1,lambdaIndex] <- 1:p
+ A2[,2:(m+1),lambdaIndex] <- discardedVariables
+ Rho[,,,lambdaIndex] <- rho
+ Pi[,lambdaIndex] <- pi
+ }
+ }
+ }
+
+ return(res = list(A1 = A1, A2 = A2 , Rho = Rho, Pi = Pi))
+}
projects / valse.git / blobdiff